DETAILED ACTION
The following is a Non-Final Office Action in response to the Election to the Restriction/Election Requirement received on 21 May 2026. Claims 1-8 and 15-20 have been cancelled. Claims 12 and 13 have been amended. Claims 21-34 have been added. Claims 9-14 and 21-34 are now pending in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Election/Restrictions
Applicant’s election without traverse of group II, claims 9-14, in the reply filed on 21 May 2026 is acknowledged.
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 27 February 2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 9-14 and 21-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s), in part, receiving a raw sensor data packet from a sensor device, the raw sensor data packet comprising a raw value indicative of a measured process variable associated with an operation and a sensor identifier associated with the sensor device; identifying a translation function associated with the sensor device that is maintained by a control system for the operation using the sensor identifier associated with the sensor device; applying the raw value indicative of the measured process variable associated with the operation to the translation function to generate a translated value; determining a value for the measured process variable associated with the operation based on the translated value; and controlling the operation based on the value for the measured process variable. This judicial exception is not integrated into a practical application because the claims are directed to abstract ideas of concepts performed in the human mind (mental process -- identifying a translation function associated with the sensor device that is maintained by a control system for the operation using the sensor identifier associated with the sensor device; applying the raw value indicative of the measured process variable associated with the operation to the translation function to generate a translated value) and mathematical concepts (determining a value for the measured process variable associated with the operation based on the translated value). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are directed to abstract ideas and extra-solution activities that do not have a physical or tangible form, such as mere data gathering, insignificant application, and/or mere instructions to apply a judicial exception.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 9-14 is directed to one or more non-transitory computer-readable storage media.
Claims 21-28 are directed to a control system.
Claims 29-34 are directed to a method.
Claims 9-14 and 21-34 are directed to at least one of the four statutory categories.
Step 2A, Prong One, Judicial Exception Recited?
Claims 9-14 and 21-34 are directed to abstract ideas of concepts performed in the human mind (mental process -- identifying a translation function associated with the sensor device that is maintained by a control system for the operation using the sensor identifier associated with the sensor device; applying the raw value indicative of the measured process variable associated with the operation to the translation function to generate a translated value) and mathematical concepts (determining a value for the measured process variable associated with the operation based on the translated value) given the broadest reasonable interpretation.
As per claims 9, 21 and 29, these claims similarly recite the limitations of “receiving a raw sensor data packet from a sensor device, the raw sensor data packet comprising a raw value indicative of a measured process variable associated with an operation and a sensor identifier associated with the sensor device; identifying a translation function associated with the sensor device that is maintained by a control system for the operation using the sensor identifier associated with the sensor device; applying the raw value indicative of the measured process variable associated with the operation to the translation function to generate a translated value; determining a value for the measured process variable associated with the operation based on the translated value.” As drafted, these limitations encompass concepts performed in the human mind and mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claims 10, 22 and 30, these claims similarly recite the limitations of “receiving the raw value indicative of the measured process variable associated with the operation comprises receiving a raw digital value that has not been manipulated by the sensor device.” As drafted, these limitations encompass concepts performed in the human mind.
As per claims 11, 23 and 31, these claims similarly recite the limitations of “identifying a corrective function associated with the sensor device that is maintained by the control system for the operation using the sensor identifier associated with the sensor device; and applying the sensor operational data to the corrective function to generate a corrective value; wherein determining the value for the measured process variable associated with the operation based on the translated value comprises determining the value for the measured process variable associated with the operation based on both the translated value and the corrective value.” As drafted, these limitations encompass concepts performed in the human mind and mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claims 13, 25 and 33, these claims similarly recite the limitations of “receive a second raw sensor data packet from a second sensor device; calculate a second value for a second measured process variable associated with the operation based on the second raw sensor data packet; generate a virtual sensor, wherein a third value associated with the virtual sensor is indicative of a third measured process variable associated with the operation; and calculate the third value based on both the value for the measured process variable and the second value for the second measured process variable.” As drafted, these limitations encompass concepts performed in the human mind and mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claims 14, 26 and 34, these claims similarly recite the limitations of “calculate the third value by inferring the third value from the value for the measured process variable and the second value for the second measured process variable; and validate at least one of the value for the measured process variable or the second value for the second measured process variable based on the third value.” As drafted, these limitations encompass concepts performed in the human mind and mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
Claims 9-14 and 21-34 are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 9-14 and 21-34 are directed to abstract ideas (mathematical concepts and concepts performed in the human mind).
Step 2A, Prong Two, Integrated into a Practical Application?
The claims recite the following additional limitations:
As per claims 12, 24 and 32, these claims similarly recite the limitations of “the sensor operational data comprises temperature data indicative of an operational temperature associated with the sensor device at the time when the sensor device generated the raw sensor data packet.”
As per claim 27, this claim recites the limitation of “the memory and the processing circuitry are implemented on one or more cloud servers installed at a remote location relative to a facility associated with the operation.”
As per claim 28, this claim recites the limitation of “the memory and the processing circuitry are implemented on one or more on-premises servers installed at a facility associated with the operation.”
As drafted, these limitations encompass no more than mere instructions to apply an exception. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
The additional elements recite mere instructions to apply an exception and do not provide integration into a practical application because they do no more than merely invoke computers or machinery as a tool to perform an existing process. The additional claim limitations, claim elements together and claims in their entirety do not provide integration into a practical application. The additional claim limitations, claim elements together and claims in their entirety do not integrate the abstract idea into a practical application or provide an inventive concept (significantly more than the abstract idea). The concept described in the claim(s) is not meaningfully different than those concepts found by the courts to be abstract ideas. As such, the description in the claims describes the concept identified as an abstract idea (data gathering, data outputting and data transmission). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not integrate the exception into a practical application of the exception.
Claims 9-14 and 21-34 do not integrate the recited abstract ideas into a practical application.
Step 2B, Inventive Concept (Significantly More)?
When considered both individually and as an ordered combination, the additional elements and elements of claims 9-14 and 21-34 do not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract ideas into a practical application. The additional elements outlined in Step 2A performing functions as designed simply accomplish execution of the abstract ideas. The additional limitations identified as no more than mere instructions to apply an exception above are carried over and they also do not provide significantly more.
As per claims 9-14 and 21-34, these claims similarly recite the limitations of “receiving a raw sensor data packet from a sensor device, the raw sensor data packet comprising a raw value indicative of a measured process variable associated with an operation and a sensor identifier associated with the sensor device; identifying a translation function associated with the sensor device that is maintained by a control system for the operation using the sensor identifier associated with the sensor device; applying the raw value indicative of the measured process variable associated with the operation to the translation function to generate a translated value; determining a value for the measured process variable associated with the operation based on the translated value; and controlling the operation based on the value for the measured process variable.” As drafted, these limitations encompass no more than mere instructions to apply an exception. See MPEP 2106.05(f).
Considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. Hence, the claims are not patent eligible.
Claims 9-14 and 21-34 are therefore drawn to ineligible subject matter as they remain directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 9, 10, 13, 21, 22, 25, 29, 30 and 33 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US Pub. No. 2021/0360071 A1 (USPN 12,137,153 B2) to CHAKRABORTY et al.
As per claim 9, the CHAKRABORTY et al. reference discloses one or more non-transitory computer-readable storage media having instructions (see [0104], “computer executable instructions”) stored thereon that, when executed by one or more processors (“one or more processors 719”), cause the one or more processors (“one or more processors 719”) to implement operations comprising: receiving a raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a sensor device (“sensors 130-136”), the raw sensor data packet (“data (e.g., raw data)”) comprising a raw value (see [0034], “raw data values”) indicative of a measured process variable (“data values”) associated with an operation (see [0048], “process 400”) and a sensor identifier (see [0055], “sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); identifying a translation function (see [0023], “data conversion model”) associated with the sensor device (“sensors 130-136”) that is maintained by a control system (see [0019], “sensor management device 102”) for the operation (“process 400”) using the sensor identifier (“sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); applying the raw value (“raw data values”) indicative of the measured process variable (“data values”)associated with the operation (“process 400”) to the translation function (“data conversion model”) to generate a translated value (see [0034], “degrees Fahrenheit”); determining a value (“degrees Fahrenheit”) for the measured process variable (“temperature data”) associated with the operation (“process 400”) based on the translated value (“degrees Fahrenheit”); and controlling the operation (“process 400”) based on the value (“degrees Fahrenheit”) for the measured process variable (“temperature data”).
As per claim 10, the CHAKRABORTY et al. reference discloses receiving the raw value (“raw data values”) indicative of the measured process variable (“data values”) associated with the operation (“process 400”) comprises receiving a raw digital value (see [0029], “digital”) that has not been manipulated (“collect differing types of data”) by the sensor device (“sensors 130-136”).
As per claim 13, the CHAKRABORTY et al. reference discloses the operations (“process 400”) comprising: receiving a second raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a second sensor device (“sensors 130-136”); calculating a second value (see [0034], “raw data values”) for a second measured process variable (“data values”) associated with the operation (see [0048], “process 400”) based on the second raw sensor data packet (“data (e.g., raw data)”); generating a virtual sensor (see [0020], “sensor abstraction layer 120 (SAL)”), wherein a third value (“raw data values”) associated with the virtual sensor (“sensor abstraction layer 120 (SAL)”) is indicative of a third measured process variable (“data values”) associated with the operation (“process 400”); and calculating the third value (“raw data values”) based on both the value for the measured process variable (“data values”) and the second value (“raw data values”) for the second measured process variable (“data values”).
As per claim 21, the CHAKRABORTY et al. reference discloses a control system, comprising: memory (see [0105], “memory 722”) comprising machine-readable instructions (see [0104], “computer executable instructions”); and processing circuitry (“one or more processors 719”) configured to execute the machine-readable instructions (“computer executable instructions”) to: receive a raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a sensor device (“sensors 130-136”), the raw sensor data packet (“data (e.g., raw data)”) comprising a raw value (see [0034], “raw data values”) indicative of a measured process variable (“data values”) associated with an operation (see [0048], “process 400”) and a sensor identifier (see [0055], “sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); identify a translation function (see [0023], “data conversion model”) associated with the sensor device (“sensors 130-136”) that is maintained by a control system (see [0019], “sensor management device 102”) for the operation (“process 400”) using the sensor identifier (“sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); apply the raw value (“raw data values”) indicative of the measured process variable (“data values”) associated with the operation (“process 400”) to the translation function (“data conversion model”) to generate a translated value (see [0034], “degrees Fahrenheit”); determine a value (“degrees Fahrenheit”) for the measured process variable (“temperature data”) associated with the operation (“process 400”) based on the translated value (“degrees Fahrenheit”); and control the (“process 400”) based on the value (“degrees Fahrenheit”) for the measured process variable (“temperature data”).
As per claim 22, the CHAKRABORTY et al. reference discloses the processing circuitry (“one or more processors 719”) is configured to execute the machine-readable instructions (“computer executable instructions”) to receive the raw value (“raw data values”) indicative of the measured process variable (“data values”) associated with the operation (“process 400”) comprises receiving a raw digital value (see [0029], “digital”) that has not been manipulated (“collect differing types of data”) by the sensor device (“sensors 130-136”).
As per claim 25, the CHAKRABORTY et al. reference discloses the processing circuitry (“one or more processors 719”) is configured to execute the machine-readable instructions (“computer executable instructions”) to: receive a second raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a second sensor device (“sensors 130-136”); calculate a second value (see [0034], “raw data values”) for a second measured process variable (“data values”) associated with the operation (see [0048], “process 400”) based on the second raw sensor data packet (“data (e.g., raw data)”); generate a virtual sensor (see [0020], “sensor abstraction layer 120 (SAL)”), wherein a third value (“raw data values”) associated with the virtual sensor (“sensor abstraction layer 120 (SAL)”) is indicative of a third measured process variable (“data values”) associated with the operation (“process 400”); and calculate the third value (“raw data values”) based on both the value for the measured process variable (“data values”) and the second value (“raw data values”) for the second measured process variable (“data values”).
As per claim 29, the CHAKRABORTY et al. reference discloses a method, comprising: receiving a raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a sensor device (“sensors 130-136”), the raw sensor data packet (“data (e.g., raw data)”) comprising a raw value (see [0034], “raw data values”) indicative of a measured process variable (“data values”) associated with an operation (see [0048], “process 400”) and a sensor identifier (see [0055], “sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); identifying a translation function (see [0023], “data conversion model”) associated with the sensor device (“sensors 130-136”) that is maintained by a control system (see [0019], “sensor management device 102”) for the operation (“process 400”) using the sensor identifier (“sensor identifier or ID”) associated with the sensor device (“sensors 130-136”); applying the raw value (“raw data values”) indicative of the measured process variable (“data values”) associated with the operation (“process 400”) to the translation function (“data conversion model”) to generate a translated value (see [0034], “degrees Fahrenheit”); determining a value (“degrees Fahrenheit”) for the measured process variable (“temperature data”) associated with the operation (“process 400”) based on the translated value (“degrees Fahrenheit”); and controlling the operation (“process 400”) based on the value (“degrees Fahrenheit”) for the measured process variable (“temperature data”).
As per claim 30, the CHAKRABORTY et al. reference discloses receiving the raw value (“raw data values”) indicative of the measured process variable (“data values”) associated with the operation (“process 400”) comprises receiving a raw digital value (see [0029], “digital”) that has not been manipulated (“collect differing types of data”) by the sensor device (“sensors 130-136”).
As per claim 33, the CHAKRABORTY et al. reference discloses receiving a second raw sensor data packet (see [0019], “collect data (e.g., raw data)”) from a second sensor device (“sensors 130-136”); calculating a second value (see [0034], “raw data values”) for a second measured process variable (“data values”) associated with the operation (see [0048], “process 400”) based on the second raw sensor data packet (“data (e.g., raw data)”); generating a virtual sensor (see [0020], “sensor abstraction layer 120 (SAL)”), wherein a third value (“raw data values”) associated with the virtual sensor (“sensor abstraction layer 120 (SAL)”) is indicative of a third measured process variable (“data values”) associated with the operation (“process 400”); and calculating the third value (“raw data values”) based on both the value for the measured process variable (“data values”) and the second value (“raw data values”) for the second measured process variable (“data values”).
Claim(s) 9-13, 21-25 and 27-33 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Pub. No. 2025/0054075 A1 to YOUNG et al.
As per claim 9, the YOUNG et al. reference discloses one or more non-transitory computer-readable storage media having instructions (see [0101], “instructions”), stored thereon that, when executed by one or more processors (“processor 710”), cause the one or more processors (“processor 710”) to implement operations (see [0102], “operations”) comprising: receiving a raw sensor data packet (see [0024], “raw data”) from a sensor device (see [0032], “sensor 144”), the raw sensor data packet (“raw data”) comprising a raw value (“raw data”) indicative of a measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with an operation (“operations”) and a sensor identifier (see [0033], “unique identifier”) associated with the sensor device (“sensor 144”); identifying a translation function (see [0056], “functions-based translation (e.g., a process 110)”) associated with the sensor device (“sensor 144”) that is maintained by a control system (see [0024], “data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); applying the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) to the translation function (“functions-based translation (e.g., a process 110)”) to generate a translated value (see [0024], “normalized data”); determining a value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the translated value (“normalized data”); and controlling the operation (“operations”) based on the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
As per claim 10, the YOUNG et al. reference discloses receiving the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) comprises receiving a raw digital value (see [0102], “digital electronic circuitry”) that has not been manipulated (see [0083], “original, raw data”) by the sensor device (“sensor 144”).
As per claim 11, the YOUNG et al. reference discloses the raw sensor data packet (“raw data”) comprises sensor operational data (see [0019], “temporal and geospatial tags”) indicative of an operational state (see [0024], “error detector 114”) of the sensor device (“sensor 144”) at a time when the sensor device (“sensor 144”) generated the raw sensor data packet (“raw data”), the operations (“operations”) comprising: identifying a corrective function (see [0072], “translator 108, process 110”) associated with the sensor device (“sensor 144”) that is maintained by the control system (“data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); and applying the sensor operational data to the corrective function (“translator 108, process 110”) to generate a corrective value (“corrected, re-translated portion”); wherein determining the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the translated value (“normalized data”) comprises determining the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on both the translated value (“normalized data”) and the corrective value (“corrected, re-translated portion”).
As per claim 12, the YOUNG et al. reference discloses the sensor operational data (“temporal and geospatial tags”) comprises temperature data (see [0037], “temperature data”) indicative of an operational temperature (“temperature data”) associated with the sensor device (see [0043], “sensors 144”) at the time (see [0037], “timestamp”) when the sensor device (“sensors 144”) generated the raw sensor data packet (see [0026], “raw data 122”).
As per claim 13, the YOUNG et al. reference discloses the operations comprising: receiving a second raw sensor data packet (see [0026], “raw data 122”) from a second sensor device (see [0043], “sensors 144”); calculating a second value (“raw data”) for a second measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the second raw sensor data packet (“raw data 122”); generating a virtual sensor (see [0106], “virtual machine”), wherein a third value (“raw data”) associated with the virtual sensor (“virtual machine”) is indicative of a third measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”); and calculating the third value (“raw data”) based on both the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) and the second value (“raw data”) for the second measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
As per claim 21, the YOUNG et al. reference discloses a control system, comprising: memory (see [0101], “memory 715”) comprising machine-readable instructions (see [0101], “instructions”); and processing circuitry (“processor 710”) configured to execute the machine-readable instructions (“instructions”) to: receive a raw sensor data packet (see [0024], “raw data”) from a sensor device (see [0032], “sensor 144”), the raw sensor data packet (“raw data”) comprising a raw value (“raw data”) indicative of a measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with an operation (“operations”) and a sensor identifier (see [0033], “unique identifier”) associated with the sensor device (“sensor 144”); identify a translation function (see [0056], “functions-based translation (e.g., a process 110)”) associated with the sensor device (“sensor 144”) that is maintained by a control system (see [0024], “data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); apply the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) to the translation function (“functions-based translation (e.g., a process 110)”) to generate a translated value (see [0024], “normalized data”); determine a value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the translated value (“normalized data”); and control the operation (“operations”) based on the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
As per claim 22, the YOUNG et al. reference discloses the processing circuitry (“processor 710”) is configured to execute the machine-readable instructions (“instructions”) to receive the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) as a raw digital value (see [0102], “digital electronic circuitry”) that has not been manipulated (see [0083], “original, raw data”) by the sensor device (“sensor 144”).
As per claim 23, the YOUNG et al. reference discloses the raw sensor data packet (“raw data”) comprises sensor operational data (see [0019], “temporal and geospatial tags”) indicative of an operational state (see [0024], “error detector 114”) of the sensor device (“sensor 144”) at a time when the sensor device (“sensor 144”) generated the raw sensor data packet (“raw data”), and the processing circuitry (“processor 710”) is configured to execute the machine-readable instructions (“instructions”) to: identify a corrective function (see [0072], “translator 108, process 110”) associated with the sensor device (“sensor 144”) that is maintained by the control system (“data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); and apply the sensor operational data to the corrective function (“translator 108, process 110”) to generate a corrective value (“corrected, re-translated portion”); and determine the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on both the translated value (“normalized data”) and the corrective value (“corrected, re-translated portion”).
As per claim 24, the YOUNG et al. reference discloses the sensor operational data (“temporal and geospatial tags”) comprises temperature data (see [0037], “temperature data”) indicative of an operational temperature (“temperature data”) associated with the sensor device (see [0043], “sensors 144”) at the time (see [0037], “timestamp”) when the sensor device (“sensors 144”) generated the raw sensor data packet (see [0026], “raw data 122”).
As per claim 25, the YOUNG et al. reference discloses the processing circuitry (“processor 710”) is configured to execute the machine-readable instructions (“instructions”) to: receive a second raw sensor data packet (see [0026], “raw data 122”) from a second sensor device (see [0043], “sensors 144”); calculate a second value (“raw data”) for a second measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the second raw sensor data packet (“raw data 122”); generate a virtual sensor (see [0106], “virtual machine”), wherein a third value (“raw data”) associated with the virtual sensor (“virtual machine”) is indicative of a third measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”); and calculate the third value (“raw data”) based on both the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) and the second value (“raw data”) for the second measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
As per claim 27, the YOUNG et al. reference discloses the memory (“memory 715”) and the processing circuitry (“processor 710”) are implemented on one or more cloud servers (see [0044], “cloud computing environment”) installed at a remote location (“remote data source 140”) relative to a facility (see [0047], “farm”) associated with the operation (“operations”).
As per claim 28, the YOUNG et al. reference discloses the memory (“memory 715”) and the processing circuitry (“processor 710”) are implemented on one or more on-premises servers (see [0031], “local, computing devices”) installed at a facility (“farm”) associated with the operation (“operations”).
As per claim 29, the YOUNG et al. reference discloses a method comprising: receiving a raw sensor data packet (see [0024], “raw data”) from a sensor device (see [0032], “sensor 144”), the raw sensor data packet (“raw data”) comprising a raw value (“raw data”) indicative of a measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with an operation (“operations”) and a sensor identifier (see [0033], “unique identifier”) associated with the sensor device (“sensor 144”); identifying a translation function (see [0056], “functions-based translation (e.g., a process 110)”) associated with the sensor device (“sensor 144”) that is maintained by a control system (see [0024], “data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); applying the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) to the translation function (“functions-based translation (e.g., a process 110)”) to generate a translated value (see [0024], “normalized data”); determining a value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the translated value (“normalized data”); and controlling the operation (“operations”) based on the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
As per claim 30, the YOUNG et al. reference discloses receiving the raw value (“raw data”) indicative of the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) comprises receiving a raw digital value (see [0102], “digital electronic circuitry”) that has not been manipulated (see [0083], “original, raw data”) by the sensor device (“sensor 144”).
As per claim 31, the YOUNG et al. reference discloses the raw sensor data packet (“raw data”) comprises sensor operational data (see [0019], “temporal and geospatial tags”) indicative of an operational state (see [0024], “error detector 114”) of the sensor device (“sensor 144”) at a time when the sensor device (“sensor 144”) generated the raw sensor data packet (“raw data”), the method comprising: identifying a corrective function (see [0072], “translator 108, process 110”) associated with the sensor device (“sensor 144”) that is maintained by the control system (“data processing system 102”) for the operation (“operations”) using the sensor identifier (“unique identifier”) associated with the sensor device (“sensor 144”); and applying the sensor operational data to the corrective function (“translator 108, process 110”) to generate a corrective value (“corrected, re-translated portion”); wherein determining the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the translated value (“normalized data”) comprises determining the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on both the translated value (“normalized data”) and the corrective value (“corrected, re-translated portion”).
As per claim 32, the YOUNG et al. reference discloses the sensor operational data (“temporal and geospatial tags”) comprises temperature data (see [0037], “temperature data”) indicative of an operational temperature (“temperature data”) associated with the sensor device (see [0043], “sensors 144”) at the time (see [0037], “timestamp”) when the sensor device (“sensors 144”) generated the raw sensor data packet (see [0026], “raw data 122”).
As per claim 33, the YOUNG et al. reference discloses receiving a second raw sensor data packet (see [0026], “raw data 122”) from a second sensor device (see [0043], “sensors 144”); calculating a second value (“raw data”) for a second measured process variable (see [0032], “temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”) based on the second raw sensor data packet (“raw data 122”); generating a virtual sensor (see [0106], “virtual machine”), wherein a third value (“raw data”) associated with the virtual sensor (“virtual machine”) is indicative of a third measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) associated with the operation (“operations”); and calculating the third value (“raw data”) based on both the value for the measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”) and the second value (“raw data”) for the second measured process variable (“temperature sensor, light sensor, ambient light sensor, wind sensor, precipitation sensor, humidity sensor, or soil moisture probe”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to raw sensor data input to control system:
US 12259351 B2 to Simpson et al.
US 11837348 B2 to Davis et al.
CN 115770040 B to BHAVARAJU et al.
CA 3133339 A1 to SMITH et al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystal J Barnes-Bullock whose telephone number is (571)272-3679. The examiner can normally be reached Monday - Friday 8 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CRYSTAL J BARNES-BULLOCK/Primary Examiner, Art Unit 2117 7 August 2026
Prosecution Insights